AI and the Clinical Immunology/Immunoinformatics for COVID-19

Artificial Intelligence (AI) is contributing to the campaign against the Coronavirus Disease 2019 (COVID-19). Since 2019, more and more AI frameworks and applications in COVID-19 have been proposed, and the recent research has shown that AI is a promising technology because AI can achieve a higher degree of scalability, a more comprehensive and identification of patterns in the vast amount of unstructured and noisy data, accelerated processing power, and strategies to outperform traditional methods in many specific tasks. In this chapter, we focus on the specific AI applications in the clinical immunology/immunoinformatics for COVID-19. More precisely, on one hand, we discuss the application of deep learning in designing SARS-CoV-2 vaccines, and, on the other hand, we discuss the development of a machine learning framework for investigating the SARS-CoV-2 mutations that can help us better respond to the future mutant viruses, including designing more robust vaccines based on such AI approaches. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Optimization and Deoptimization of Codons in SARS-CoV-2 and Related Implications for Vaccine Development.

The spread of coronavirus disease 2019 (COVID-19), caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), has progressed into a global pandemic. To date, thousands of genetic variants have been identified among SARS-CoV-2 isolates collected from patients. Sequence analysis reveals that the codon adaptation index (CAI) values of viral sequences have decreased over time but with occasional fluctuations. Through evolution modeling, it is found that this phenomenon may result from the virus's mutation preference during transmission. Using dual-luciferase assays, it is further discovered that the deoptimization of codons in the viral sequence may weaken protein expression during virus evolution, indicating that codon usage may play an important role in virus fitness. Finally, given the importance of codon usage in protein expression and particularly for mRNA vaccines, it is designed several codon-optimized Omicron BA.2.12.1, BA.4/5, and XBB.1.5 spike mRNA vaccine candidates and experimentally validated their high levels of expression. This study highlights the importance of codon usage in virus evolution and provides guidelines for codon optimization in mRNA and DNA vaccine development.

In Silico Structure-Based Vaccine Design.

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

Post-translational modifications: Host defence mechanism, pathogenic weapon, and emerged target of anti-infective drugs

Post-translational modifications are changes introduced to proteins after their translation. They are the means to generate molecular diversity, expand protein function, control catalytic activity and trigger quick responses to a wide range of stimuli. Moreover, they regulate numerous biological processes, including pathogen invasion and host defence mechanisms. It is well established that bacteria and viruses utilize post-translational modifications on their own or their host's proteins to advance their pathogenicity. Doing so, they evade immune responses, target signaling pathways and manipulate host cytoskeleton to achieve survival, replication and propagation. Many bacterial species secrete virulence factors into the host and mediate hostpathogen interactions by inducing post-translational modifications that subvert fundamental cellular processes. Viral pathogens also utilize post translational modifications in order to overcome the host defence mechanisms and hijack its cellular machinery for their replication and propagation. For example, many coronavirus proteins are modified to achieve host invasion, evasion of immune responses and utilization of the host translational machinery. PTMs are also considered potential targets for the development of novel therapeutics from natural products with antibiotic properties, like lasso peptides and lantibiotics. The last decade, significant progress was made in understanding the mechanisms that govern PTMs and mediate regulation of protein structure and function. This urges the identification of relevant molecular targets, the design of specific drugs and the discovery of PTM-based medicine. Therefore, PTMs emerge as a highly promising field for the investigation and discovery of new therapeutics for many infectious diseases.Copyright © 2021 Bentham Science Publishers.

Glycan masking in vaccine design: Targets, immunogens and applications.

Glycan masking is a novel technique in reverse vaccinology in which sugar chains (glycans) are added on the surface of immunogen candidates to hide regions of low interest and thus focus the immune system on highly therapeutic epitopes. This shielding strategy is inspired by viruses such as influenza and HIV, which are able to escape the immune system by incorporating additional glycosylation and preventing the binding of therapeutic antibodies. Interestingly, the glycan masking technique is mainly used in vaccine design to fight the same viruses that naturally use glycans to evade the immune system. In this review we report the major successes obtained with the glycan masking technique in epitope-focused vaccine design. We focus on the choice of the target antigen, the strategy for immunogen design and the relevance of the carrier vector to induce a strong immune response. Moreover, we will elucidate the different applications that can be accomplished with glycan masking, such as shifting the immune response from hyper-variable epitopes to more conserved ones, focusing the response on known therapeutic epitopes, broadening the response to different viral strains/sub-types and altering the antigen immunogenicity to elicit higher or lower immune response, as desired.

Proteome Exploration of human coronaviruses for Identifying Novel vaccine candidate: A Hierarchical Subtractive Genomics and Reverse Vaccinology Approach.

BACKGROUND: The SARSCoV-2 is responsible for infecting more than 271,000,000 people in 222 countries by December 10, of which 5,300,000 have died. COVID-19 was introduced by World Health Organization as a global concern and a pandemic disease due to the prevalence of disease. OBJECTIVES: Developing of preventive or therapeutics medication against novel-cov2019 is an urgent need, and has high priority among scientific societies, in this regard, the production of effective vaccines is one of the most significant and high-priority necessity. To date specific antiviral therapeutic and prophylactic vaccine for novel coronavirus (n-CoV2019) are not available. Because of costing and time-consuming of experimental strategies during vaccine design procedure, different immunoinformatics methods were developed. Recently Because of defect study on proteins of n-cov2019, its recommended to study other human coronaviruses. METHODS: At the beginning of vaccine design, the proteome study is essential. In this investigation, the whole human coronaviruses proteome was evaluated using proteome subtraction strategy. Out of 5945 human coronavirus proteins, five new antigenic proteins were selected by analyzing the hierarchical proteome subtraction and then their various physicochemical and immunological properties were also investigated bioinformatically. RESULTS: All five protein sequences are antigenic and non-allergenic proteins; moreover, spike protein group including Spike glycoprotein (E2) (Peplomer protein), spike fragment and spike glycoprotein fragment showed acceptable stability, which can be used to design new vaccines against human coronaviruses. CONCLUSION: These selected peptides and the other introduced protein in this study (HE, orf7a, SARS_X4 domain-containing protein and protein 8) can be employed as a suitable candidate for developing novel prophylactic or therapeutic vaccine against human coronaviruses.

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

Advanced Vaccine Design Strategies against SARS-CoV-2 and Emerging Variants.

Vaccination is the most cost-effective means in the fight against infectious diseases. Various kinds of vaccines have been developed since the outbreak of COVID-19, some of which have been approved for clinical application. Though vaccines available achieved partial success in protecting vaccinated subjects from infection or hospitalization, numerous efforts are still needed to end the global pandemic, especially in the case of emerging new variants. Safe and efficient vaccines are the key elements to stop the pandemic from attacking the world now; novel and evolving vaccine technologies are urged in the course of fighting (re)-emerging infectious diseases. Advances in biotechnology offered the progress of vaccinology in the past few years, and lots of innovative approaches have been applied to the vaccine design during the ongoing pandemic. In this review, we summarize the state-of-the-art vaccine strategies involved in controlling the transmission of SARS-CoV-2 and its variants. In addition, challenges and future directions for rational vaccine design are discussed.

In silico design of a multi-epitope vaccine against the spike and the nucleocapsid proteins of the Omicron variant of SARS-CoV-2.

Computational studies can comprise an effective approach to treating and preventing viral infections. Since 2019, the world has been dealing with the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The most important achievement in this short period of time in the effort to reduce morbidity and mortality was the production of vaccines and effective antiviral drugs. Although the virus has been significantly suppressed, it continues to evolve, spread, and evade the host's immune system. Recently, researchers have turned to immunoinformatics tools to reduce side effects and save the time and cost of traditional vaccine production methods. In the present study, an attempt has been made to design a multi-epitope vaccine with humoral and cellular immune response stimulation against the Omicron variant of SARS-CoV-2 by investigating new mutations in spike (S) and nucleocapsid (N) proteins. The population coverage of the vaccine was evaluated as appropriate compared to other studies. The results of molecular dynamics simulation and molecular mechanics/generalized Born surface area (MM/GBSA) calculations predict the stability and proper interaction of the vaccine with Toll-like receptor 4 (TLR-4) as an innate immune receptor. The results of the immune simulation show a significant increase in the coordinated response of IgM and IgG after the third injection of the vaccine. Also, in the continuation of the research, spike proteins from BA.4 and BA.5 lineages were screened by immunoinformatics filters and effective epitopes were suggested for vaccine design. Despite the high precision of computational studies, in-vivo and in-vitro research is needed for final confirmation.Communicated by Ramaswamy H. Sarma.

Strategies for Vaccine Design for Coronavirus All Variants of Concern Using Immunoinformatics Techniques

Aims: A short sequence of viral protein/ peptide could be used as a potential vaccine to treat coronavirus. Considering all variants of concern (VOC), designing a peptide vaccine for severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) is a challenging task for scientists. Materials & Methods: In this study, an epitope-containing vaccine peptide in nonstructural protein 4 (nsp4) of SARS-CoV-2 was predicted. Using a modified method for both B and T cell epitope prediction (verified by molecular docking studies), linear B and T cell epitopes of nsp4 protein were predicted. Predicted epitopes were analyzed with population coverage calculation and epitope conservancy analysis. Findings: The short peptide sequence74QRGGSYTNDKA84 was selected as B-cell epitope by considering the scores of surface accessibility, hydrophilicity, and beta turn for each amino acid residue. Similarly, the peptide sequences 359 FLAHIQWMV367 and359FLAHIQWVMFTPLV373 were predicted as T cell epitopes for MHC-I and MHC-II molecules. These two potential epitopes could favor HLA-A*02:01 and HLA-DRB*01:01 as MHC allelic proteins with the lowest IC50 values, respectively. No amino acid mutations were observed in GISAID (global initiative on sharing all influenza data) database for alpha, beta, gamma, and delta variants of concerns. Among seven amino acid point mutations in nsp4 protein of omicron variant, none were present in the peptide sequences of the predicted epitopes. Conclusion: Short peptide sequences could be predicted as vaccines to prevent infections caused by coronavirus variants of concerns. © 2022, TMU Press.

Increased B Cell Understanding Puts Improved Vaccine Platforms Just Over the Horizon.

In the face of an endlessly expanding repertoire of Ags, vaccines are constantly being tested, each more effective than the last. As viruses and other pathogens evolve to become more infectious, the need for efficient and effective vaccines grows daily, which is especially obvious in an era that is still attempting to remove itself from the clutches of the severe acute respiratory syndrome coronavirus 2, the cause of coronavirus pandemic. To continue evolving alongside these pathogens, it is proving increasingly essential to consider one of the main effector cells of the immune system. As one of the chief orchestrators of the humoral immune response, the B cell and other lymphocytes are essential to not only achieving immunity, but also maintaining it, which is the vital objective of every vaccine.

Functional studies of HLA and its role in SARS-CoV-2: Stimulating T cell response and vaccine development.

In the biological immune process, the major histocompatibility complex (MHC) plays an indispensable role in the expression of HLA molecules in the human body when viral infection activates the T-cell response to remove the virus. Since the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 2019, how to address and prevent SARS-CoV-2 has become a common problem facing all mankind. The T-cell immune response activated by MHC peptides is a way to construct a defense line and reduce the transmission and harm of the virus. Presentation of SARS-CoV-2 antigen is associated with different types of HLA phenotypes, and different HLA phenotypes induce different immune responses. The prediction of SARS-CoV-2 mutation information and the design of vaccines based on HLAs can effectively activate autoimmunity and cope with virus mutations, which can provide some references for the prevention and treatment of SARS-CoV-2.

Designing multi-epitope based peptide vaccine targeting spike protein SARS-CoV-2 B1.1.529 (Omicron) variant using computational approaches.

Millions of lives have been infected since the SARS-CoV-2 outbreak in 2019. The high human-to-human transmission rate has warranted a need for a vaccine to protect people. Although some vaccines are in use, due to the high mutation rate in the SARS-CoV-2 multiple variants, the current vaccines may not be sufficient to immunize people against new variant threats. One of the emerging concern variants is B1.1.529 (Omicron), which carries ~ 30 mutations in the Spike protein (S) of SARS-CoV-2 and is predicted to evade antibody recognition even from vaccinated people. We used a structure-based approach and an epitope prediction server to develop a Multi-Epitope based Subunit Vaccine (MESV) involving SARS-CoV-2 B1.1.529 variant spike glycoprotein. The predicted epitope with better antigenicity and non-toxicity was used for designing and predicting vaccine construct features and structure models. In addition, the MESV construct In silico cloning in the pET28a expression vector predicted the construct to be highly translational. The proposed MESV vaccine construct was also subjected to immune simulation prediction and was found to be highly antigenic and elicit a cell-mediated immune response. Therefore, the proposed MESV in the present study has the potential to be evaluated further for vaccine production against the newly identified B1.1.529 (Omicron) variant of concern. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02027-6.

Candidate Multi-Epitope Vaccine against Corona B.1.617 Lineage: In Silico Approach.

Various mutations have accumulated since the first genome sequence of SARS-CoV2 in 2020. Mutants of the virus carrying the D614G and P681R mutations in the spike protein are increasingly becoming dominant all over the world. The two mutations increase the viral infectivity and severity of the disease. This report describes an in silico design of SARS-CoV-2 multi-epitope carrying the spike D614G and P681R mutations. The designed vaccine harbors the D614G mutation that increases viral infectivity, fitness, and the P681R mutation that enhances the cleavage of S to S1 and S2 subunits. The designed multi-epitope vaccine showed an antigenic property with a value of 0.67 and the immunogenicity of the predicted vaccine was calculated and yielded 3.4. The vaccine construct is predicted to be non-allergenic, thermostable and has hydrophilic nature. The combination of the selected CTL and HTL epitopes in the vaccine resulted in 96.85% population coverage globally. Stable interactions of the vaccine with Toll-Like Receptor 4 were tested by docking studies. The multi-epitope vaccine can be a good candidate against highly infecting SARS-CoV-2 variants.

Neutralizing monoclonal antibodies elicited by mosaic RBD nanoparticles bind conserved sarbecovirus epitopes.

Increased immune evasion by SARS-CoV-2 variants of concern highlights the need for new therapeutic neutralizing antibodies. Immunization with nanoparticles co-displaying spike receptor-binding domains (RBDs) from eight sarbecoviruses (mosaic-8 RBD-nanoparticles) efficiently elicits cross-reactive polyclonal antibodies against conserved sarbecovirus RBD epitopes. Here, we identified monoclonal antibodies (mAbs) capable of cross-reactive binding and neutralization of animal sarbecoviruses and SARS-CoV-2 variants by screening single mouse B cells secreting IgGs that bind two or more sarbecovirus RBDs. Single-particle cryo-EM structures of antibody-spike complexes, including a Fab-Omicron complex, mapped neutralizing mAbs to conserved class 1/4 RBD epitopes. Structural analyses revealed neutralization mechanisms, potentials for intra-spike trimer cross-linking by IgGs, and induced changes in trimer upon Fab binding. In addition, we identified a mAb-resembling Bebtelovimab, an EUA-approved human class 3 anti-RBD mAb. These results support using mosaic RBD-nanoparticle vaccination to generate and identify therapeutic pan-sarbecovirus and pan-variant mAbs.

IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method.

In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, 'IntegralVac,' by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing deep learning tools served as a positive control to ensure successful prediction was possible. As evidenced by increased accuracy and AUC, IntegralVac improved the prediction rate of top-ranked epitopes. We also examined the compatibility between other servers' clinical checkpoint filters and IntegralVac. This was to ensure that the other servers had a means for predicting additional checkpoint filters that we wanted to implement in IntegralVac. The clinical checkpoint filters, including allergenicity, antigenicity, and toxicity, were used as additional predictors to improve IntegralVac's prediction accuracy. We generated immunogenicity scores by cross-comparing sequence inputs with each other and determining the overlap between each individual peptide sequence. The IntegralVac increased the immunogenicity prediction accuracy to 90.1% AUC and the binding affinity accuracy to 95.4% compared to the control NetMHCPan server. The IntegralVac opens new avenues for future in silico methods, by building upon established models for continued prediction accuracy improvement.

mRNA vaccines

mRNA vaccines offer enormous promise in the fight against cancer and viral diseases, due to their superiority in terms of efficacy, safety, and industrial manufacturing. In the last few decades, sequence optimization has resulted in the development of several types of mRNAs to solve the disadvantages of high mRNA immunogenicity, instability, and inefficiency. mRNA vaccines are combined with immunological adjuvants and various delivery techniques based on immunological studies. By using mRNA-delivery techniques, mRNA efficiency and stabilization can be increased aside from sequence optimization. Increased antigen reactivity provides an understanding of mRNA-induced immunity, both innate and adaptive, without the need for antibody-dependent enhancing activity. Therefore, scientists have turned to carrier-based mRNA vaccines, dendritic cell-based mRNA vaccines, and naked mRNA vaccines to solve the problem. The molecular mechanism of mRNA vaccines and the underlying process will be discussed in this chapter, delivery strategies, and relevance to Corona Virus Disease 2019 (COVID-19). © 2022 Nova Science Publishers, Inc.

In silico design of refined ferritin-SARS-CoV-2 glyco-RBD nanoparticle vaccine.

With the onset of Coronavirus disease 2019 (COVID-19) pandemic, all attention was drawn to finding solutions to cure the coronavirus disease. Among all vaccination strategies, the nanoparticle vaccine has been shown to stimulate the immune system and provide optimal immunity to the virus in a single dose. Ferritin is a reliable self-assembled nanoparticle platform for vaccine production that has already been used in experimental studies. Furthermore, glycosylation plays a crucial role in the design of antibodies and vaccines and is an essential element in developing effective subunit vaccines. In this computational study, ferritin nanoparticles and glycosylation, which are two unique facets of vaccine design, were used to model improved nanoparticle vaccines for the first time. In this regard, molecular modeling and molecular dynamics simulation were carried out to construct three atomistic models of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) receptor binding domain (RBD)-ferritin nanoparticle vaccine, including unglycosylated, glycosylated, and modified with additional O-glycans at the ferritin-RBD interface. It was shown that the ferritin-RBD complex becomes more stable when glycans are added to the ferritin-RBD interface and optimal performance of this nanoparticle can be achieved. If validated experimentally, these findings could improve the design of nanoparticles against all microbial infections.

SARS-CoV-2 nucleocapsid protein: Importance in viral infection

The coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused millions of deaths worldwide. Therefore, it is critical to understand the biological basis of SARS-CoV-2 to develop novel approaches to control its spread. The SARS-CoV-2 nucleocapsid (N) protein is an important diagnostic and potent therapeutic target of the disease, as it is involved in numerous important functions in the viral life cycle. Several studies have explained the structural and functional aspects of the SARS-CoV-2 N protein. This review summarizes the currently available data on the evolutionarily conserved N protein of SARS-CoV-2 by providing detailed information on the structural and multifunctional characteristics of the N protein. © 2022 The Author(s).

Identification and Characterization of Epithelial Cell-Derived Dense Bodies Produced upon Cytomegalovirus Infection.

Dense bodies (DB) are complex, noninfectious particles produced during CMVinfection containing envelope and tegument proteins that may be ideal candidates as vaccines. Although DB were previously described in fibroblasts, no evidence of DB formation has been shown after propagating CMV in epithelial cells. In the present study, both fibroblast MRC-5 and epithelial ARPE-19 cells were used to study DB production during CMV infection. We demonstrate the formation of epithelial cell-derived DB, mostly located as cytoplasmic inclusions in the perinuclear area of the infected cell. DB were gradient-purified, and the nature of the viral particles was confirmed using CMV-specific immunelabeling. Epithelial cell-derived DB had higher density and more homogeneous size (200-300 nm) compared to fibroblast-derived DB (100-600 nm).In agreement with previous results characterizing DB from CMV-infected fibroblasts, the pp65 tegument protein was predominant in the epithelial cell-derived DB. Our results also suggest that epithelial cells had more CMV capsids in the cytoplasm and had spherical bodies compatible with nucleus condensation (pyknosis) in cells undergoing apoptosis that were not detected in MRC-5 infected cells at the tested time post-infection. Our results demonstrate the formation of DB in CMV-infected ARPE-19 epithelial cells that may be suitable candidate to develop a multiprotein vaccine with antigenic properties similar to that of the virions while not including the viral genome.